Transcranial stimulation device and method based on electrophysiological testing

ABSTRACT

The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements and body value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.

RELATED APPLICATIONS

The present invention is a continuation of U.S. patent application Ser.No. 17/463,606 filed Sep. 1, 2021, issued U.S. Pat. No. 11,253,701,which is a continuation-in-part of claims priority to U.S. patentapplication Ser. No. 16/914,022 filed Jun. 26, 2020, which is acontinuation of and claims priority to U.S. application Ser. No.14/578,764 filed Dec. 12, 2014, issued U.S. Pat. No. 10,780,268, whichis a continuation of and claims priority to U.S. patent application Ser.No. 14/531,012, filed on Nov. 3, 2014, issued U.S. Pat. No. 8,958,882,which is a continuation-in-part of and claims priority to U.S. patentapplication Ser. No. 14/458,673, filed on Aug. 13, 2014, issued U.S.Pat. No. 8,938,301, which is a continuation-in-part of and claimspriority to U.S. patent application Ser. No. 13/742,066 filed Jan. 15,2013, issued U.S. Pat. No. 8,838,247, which is a continuation of andclaims priority to U.S. patent application Ser. No. 13/543,204 filedJul. 6, 2012, issued U.S. Pat. No. 8,380,316, which is a continuation ofand claims priority to U.S. patent application Ser. No. 12/979,419 filedon Dec. 28, 2010, issued U.S. Pat. No. 8,239,030, which is based on andclaims priority to U.S. Provisional Patent Application Ser. No.61/292,791 filed Jan. 6, 2010.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material,which is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF INVENTION

The disclosed technology relates generally to the assessment andremediation of abnormal brain and physiological functioning, and morespecifically to the utilization of transcranial stimulation and machinelearning for brain disease detection and assessment.

BACKGROUND

Traumatic brain injuries can result in physical and/or emotionaldysfunction. Post traumatic stress disorder (PTSD) symptoms are similarto those of a mild traumatic brain injury (mTBI) and the two aredifficult to differentiate using electrical assessment methodologiessuch as symptom assessments and questionnaires. In Army deployment,statistics have shown that upwards of 20% of soldiers suffer from mildtraumatic brain injury (mTBI). Head and neck injuries, including severebrain trauma, have been reported in one quarter of United States servicemembers who have been evacuated from Iraq and Afghanistan in the firstdecade of the 21st century A common cause of such injuries arises fromexposure to percussive force from explosive devices. Further, recentmilitary analysis indicates that over 90% of patients with acute mTBIwill have vestibular (inner ear balance) disorders and those vestibulardisorders are present in over 80% of persons with chronic mTBI symptoms.Likewise, stress disorders further affect numerous individuals, whetherin a military or civilian situation. Brain injuries may further beincurred from car and bicycle accidents, sports accidents, falls, andthe like. Up to 15% of persons suffering even a mild brain injury, orconcussion, will suffer from persistent symptoms for more than a year,which significantly negatively affect their ability to work and functionin daily life. It is estimated that there are currently 5.3 millionAmericans living with a disability as a result of a TBI. There areapproximately 1.5 million diagnosed brain injuries in the U.S. annually,and it is estimated that another 2 million TBIs occur but are notproperly diagnosed. Current assessment methods are either prohibitivelyexpensive or do not diagnose the root cause of the suffering. Thus,there is a need in the art to accurately and quickly assess brain injuryand associated dysfunction and then find ways to aid or enhance optimalfunctioning.

The brain is composed of about 100 billion neurons, more than 100billion support cells and between 100 and 500 trillion neuralconnections. Each neuron, support cell and neural connection isextremely delicate, and the neural connections are tiny (approximately 1micrometer). When the brain moves within the skull, such as occurs inrapid acceleration/deceleration (e.g., exposure to sudden impact and/orexplosive devices), axons within the brain can pull, stretch and tear.If there is sufficient injury to the axon or support cells, the cellwill die, either immediately or within a few days. Such damage can occurnot only in the region that suffered direct trauma but in multipleregions (e.g., diffuse axonal injury). Loss of consciousness is not aprerequisite for mild traumatic brain injury and occurs in less than 5%of mild brain injuries, and head injuries such as diffuse axonal injuryare not detectable in routine CT or MRI scan. High false negativefindings may lead to patients being undiagnosed or misdiagnosed.Unfortunately current imaging methods still lack the resolution andsensitivity to determine functional brain capacity. Rating scales andother neuropsychological and functional examination methods have longbeen used to elucidate these functional questions, but they too arefraught with false negative results and limited specificity.

With the high prevalence of age-related cognitive decline conditions,injury from falls, cerebral-vascular events, neurodegenerativeconditions (i.e., Alzheimer's Disease) and the many brain injuriesoccurring in sports and in military operation theaters, there is a needfor a rapid and portable assessment instrument that can identify mTBIand neurocognitive dysfunction (e.g., balance, processing speed), directand provide treatment interventions, track recovery progress, and aid inpeak performance or the determination of return to leisure activities orduty.

BRIEF DESCRIPTION

The disclosed technology herein provides for electronically determininga brain malady type and a severity value for a patient based on patientinput data. For instance, a brain malady may be dementia, depression,brain injury, or any other malady relating to the brain.

The method herein operates in conjunction with a transcranialstimulation device, e.g. headset, and body value measurements, alongwith machine learning and deep learning operations associating thepatient data with reference databases. Therefrom, the method generates atreatment protocol directed to the brain malady type and tailored to thepatient.

Yet another object of the disclosed technology is to providetranscranial electrical stimulation (tES) for selective stimulation,based on measures of brain activity and physiological characteristicsand measures.

The disclosed technology includes user input and feedback functionalitywithin a clinic or operational settings, whereby user measurements arecollected, compared to one or more data sets and adjustments are made tothe stimulation output. The data input can include measurement input, aswell as clinician input recognizing various patient symptoms, as well aspatient information such as lab values, medication information andgeneral patient information. Machine learning/Deep learning (ML/DL)processing techniques are used to analyze the collected data. The ML/DLprocessing techniques review the data relative to known datasets,performing learning operations, and generate the dementia type andseverity values. Based thereon the method and system generates thetreatment protocol for the patient, including stimulation modality andscalp location(s). Therefore, the active utilization of the stimulationwith additional data allows for application of the stimulation in acontrolled environment for improving the efficacy of the stimulation.

In a method of the disclosed technology, electrophysiological datarecording and analysis, with manual or automated delivery of tESproceeds as follows. Via at least one electrode and at least onereference and ground electrode and, in one or more embodiments, aplurality of electrodes, non-invasive measurements of electricalcurrents produced by the brain of a person are conducted, including inone embodiment using a low intensity electromagnetic stimulation. Thisis done while directed stimuli, such as auditory or visual stimuli orbalance tasks (for the purpose of examining brain reactions andprocessing of stimuli) are administered to the person being tested. Abrain functional abnormality in the person, based on the conducting andthe measuring, is determined. As a result of analysis of the brainelectrical activity at rest and reactions and processing of stimuli,non-invasive brain stimulation using a tES modality of direct oralternating electrical stimulation takes place via said at least oneanode electrode and said at least one cathode electrode to said brain ofsaid person.

In embodiments of the above, a single electrode is surrounded by atleast three electrodes. When the electrodes are used for stimulationpurposes, the surrounding electrodes are of opposite polarity in acluster. That is, an anode may be surrounded by three cathodes or acathode may be surrounded three anodes. A plurality of such clusters maybe utilized, such as by pre-placement in a form fitting cap or helmet.Each cluster, or any single or plurality of electrodes, may be used tosimultaneously or alternately stimulate different regions of the brain,based on the analysis described above.

In a system of embodiments of the disclosed technology, a joint brainelectro-analysis and tES system is made up of a plurality ofspaced-apart removable and replaceable electrodes arranged in a piece ofheadgear, an electroencephalography device wired to each of theelectrodes, and a transcranial electrical stimulation device wired toeach of the electrodes. In this system, upon measuring anelectroencephalography anomaly in a brain region with theelectroencephalography device, transcranial electrical stimulation isengaged to at least one anode and at least one cathode electrode of thebrain region where the anomaly was measured.

An additional device may be used for measuring physiologicalcharacteristics of a person wearing the piece of headgear. Such anadditional device may measure heart rate variability, balance, cognitiveimpairment, and/or make pathology comparisons.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a high level drawing of a device used to carry outembodiments of the disclosed technology.

FIG. 2 shows a high level block diagram of a method of carrying outembodiments of the disclosed technology.

FIG. 3 shows an exemplary processing environment including a cloud ornetwork-based processing client.

FIG. 4 shows a perspective view of a helmet with electrodes used inembodiments of the disclosed technology.

FIG. 5 illustrates one embodiment of a flow diagram for data analysis.

FIG. 6 shows electrical pathways to electrodes within a helmet of anembodiment of the disclosed technology.

FIG. 7 is a side view of an electrode with disposable electrode bootattachment used in an embodiment of the disclosed technology.

FIG. 8 illustrates another embodiment of a headgear.

FIG. 9 illustrates a data flow diagram illustrating an overview of oneembodiment of a clinical application process.

FIG. 10 illustrates a phase-based flow diagram of one embodiment of aclinical application process.

FIG. 11 illustrates a data flow diagram of one embodiment of a measuredphysiology plus input data phase of the clinical application process.

FIG. 12 illustrates a data flow diagram of one embodiment of ananalysis-combined physiology and input data phase of the clinicalapplication process.

FIG. 13 illustrates a data flow diagram of one embodiment of calculatingindividualized intervention protocols phase of the clinical applicationprocess.

FIG. 14 illustrates a data flow diagram of one embodiment of a userselection or automated launch protocols phase of the clinicalapplication process.

FIG. 15 illustrates a data flow diagram of the phase for calculating anddisplaying physiology changes of the clinical application process.

FIG. 16 illustrates a data flow diagram for a neuromodulation therapy.

FIG. 17 illustrates a computing environment including machine learningoperations for brain disease detection and assessment as part of theneuromodulation therapy.

FIG. 18 illustrates a data flow diagram of one embodiment of theelectronic analysis of patient input data for the neuromodulationtherapy.

A better understanding of the disclosed technology will be obtained fromthe following detailed description of the preferred embodiments taken inconjunction with the drawings and the attached claims.

DETAILED DESCRIPTION

Embodiments of the disclosed technology comprise systems and methods forassessing and repairing neurological pathways damaged by trauma or otherbrain-related dysfunction, as well as performing assessment andtreatment operations in a clinical setting upon multiple users. Thecollection of data across multiple users may be used for testing orvalidation of a treatment technique. The methods comprise training apatient and stimulating brain areas where a functional abnormality (suchas abnormal electrical activity outside a threshold of voltage,regularity, coherence, phase, and/or rate) has been detected. Suchfunctional abnormalities are determined based on electroencephalographytesting, a physiological test that passively monitors electrical currentof at least one electrode positioned over the head of a test subject.

Systems of the disclosed technology comprise the use of anelectroencephalogram (EEG) which functions by recording electricalactivity from the scalp. The EEG measures electrical activity producedby the firing of neurons within the brain. In addition, an event-relatedpotential (ERP) measurement may be used. An ERP, for purposes of thisdisclosure is a measured brain response that is time locked to astimulus presented to the subject.

Physiological tests/measurements may be any one of, or a combination of,the following, and are, for purposes of this disclosure, defined asfollows: Transcranial electrical stimulation (tES)—application ofnon-invasive electrical stimulation via at least one electrode which isalso usable or used for EEG measurements in embodiments of the disclosedtechnology. For purposes of this disclosure, non-invasive electricalstimulation also refers to cranial electrotherapy stimulation (CES),which is defined as small pulses of electric current along the head of asubject.

Transcranial magnetic stimulation (TMS) is electromagnetic induction toinduce weak electric currents using a rapidly changing magnetic field tocause activity in specific or general parts of the brain, and used formeasurement of cortical or distance measures of EEG and EMG for evokedresponse latency.

Low threshold transcranial magnetic stimulation (lt-TMS) iselectromagnetic frequency emitted by way of one or more sensors placedagainst the scalp to produce a focal field to cause activity in specificor general parts of the brain, and used for neuronal modulation.

Electromyography (EMG) are measurements of electrical potential ofmuscles.

Computerized neurophysiological testing (NP) is used to estimate aperson's peak level of cognitive performance. A person's raw score on atest is compared to a large general population normative sample and/orto the subjects own baseline measurement.

Force platform or balance plate—a stand-on device usable to determinebalance and/or vestibular dysfunction. The balance plate can collectand/or record balance and/or postural data, such as the center ofpressure and sway movement to analyze vestibular and balance functionunder different test conditions (e.g., unstable foam pad and eyesclosed). The velocity of movement or excursion from balance position canbe quantified for comparison to database norms. For some embodiments,the balance plate can be moved without the need for recalibration, forexample its use in outdoor settings (e.g., sports, military arena).Collected data can be synchronized by software contained in one or morecomputers, with visual input stimuli, EEG, ERP and/or other parametersfor time-locked variance measures associated with brain dysfunction. Forsome embodiments, the balance plate may be operated by way of anelectrical current connection and instructions carried out by way of acomputing device (see FIG. 8 ) or alternatively with a wirelessconnection between the plate and the computing device for portable use.

Repetitive transcranial magnetic stimulation (rTMS) includes generatinga magnetic field that influences electrical activity in the brain. InrTMS, passing current through a coil of wire generates the magneticfield, the current provided in a repetitive fashion.

Psychological disorder screening—(such as for post-traumatic stressdisorder), a component for vestibulo-ocular reflex dysfunction, acomponent for heart rate variability measures, a component forelectroencephalography measures, and/or a component for transcranialmagnetic stimulation (TMS) delivery with voltage isolator forsimultaneous amplified cortical and distally evoked potential latencymeasures and motor threshold measures.

By way of the above measurements, while non-invasively monitoring EEGreadings of one or multiple sites/regions of the brain, anomalies inneurological impulses are detected. The sites or regions of the brainare then stimulated. As little as one sensor may be used to stimulate,and this anode or cathode may be at the site where the anomaly wasdetected and may be via the same electrode used to locate the anomalyand which measured the anomalous EEG/ERP measurement. Such an electrodemay be in a helmet worn by a user and allows for positive (to increaseneural activity) or negative (to decrease neural activity) stimulationat the site where the anomaly was detected.

Such embodiments of the disclosed technology will become clearer in viewof the following description of the figures.

FIG. 1 shows a high level drawing of a device used to carry outembodiments of the disclosed technology. A helmet cap 100 comprises atleast one, or a plurality of, electrodes 106 (represented as whitedots). The helmet may be any receptacle that holds the electrodes in aposition relative to the head of a wearer, or alternatively, electrodesmay be taped or otherwise placed on the head. Earphones 102, goggles 104and/or another display device are used in embodiments of the disclosedtechnology to exhibit stimuli to a user, the stimuli used to varymeasurable brain activity. The electrodes 106 are electrically connectedto one of an electrical stimulation device 150 or electrical measuringdevice (e.g., a sensor), such as by way of amplifier 152. The sameelectrode or electrodes may be disconnected from one such device andconnected to another such device, such as by way of changing anelectrical pathway (switch) or by physically disconnecting an electricalwire from one device, and plugging into another. In embodiments of thedisclosed technology, the electrical stimulation and measuring devicesare housed within the same physical device and comprise a switch forchanging the electrical pathway, which is manually operated orcontrolled by pre-programmed instructions. In other embodiments, themeasuring device and stimulation device are in separate housings ordevices, and only one is electrically connected to the electrode orelectrodes 106 at one time. In other embodiments, the electricalstimulation and measuring devices are housed within the same physicaldevise but have separate outlets to which the electrode(s) may beunplugged and attached. Other devices, not shown, include forceplatforms (measure postural deviations of person), devices to alter thedisplay on the goggles 104, and devices to alter the sound through theearphones 102, and input devices such as a computer mouse, keyboards,and joysticks.

Referring now to visual stimuli exhibited on a display device, such asthe goggles 104 of FIG. 1 , the visual stimuli produced may be an“immersive environment,” for example a virtual reality 2- or 3-dimensionmoving “room” displayed through a virtual reality headset. The datacollected from the balance plate, heart rate monitor, EEG, and so forth,can be used in conjunction with the visual stimuli forneurophysiological trauma assessment and/or rehabilitation training. Thedata collected from this component, as well as all other components maybe linked with data collected from other components (e.g., EEG, ERP) forassessment purposes.

The system shown in FIG. 1 may further comprise a vestibular activationtest (VAT) headset permitting a computerized test that monitors thevestibulo-ocular reflex (VOR) during natural motion. A VAT headsetuseful for the systems described herein may produce images and/or recordeye movements. Images displayed in the VAT headset may be generated bycomputer-implemented instructions and transmitted via electricalimpulses to the VAT headset via wireless or direct connection. Eyemovements may be recorded by way of the VAT headset. The VOR is a reflexeye movement that stabilizes images on the retina during head movementby producing an eye movement in the direction opposite to head movement,thus preserving the image on the center of the visual field. As oculartrauma is often concomitant with traumatic brain injury, this componentallows additional assessment of injury.

In a clinical or controlled environment, the stimulation techniquesdescribed herein are enhanced based on the inclusion of additional datasources and measurements. As described in further detail below,algorithmic processing of multiple data points generate good-fitdeterminations that trigger individual treatment protocols. Thoseprotocols help define stimulation parameters, such as type ofstimulation, location, amount, duration, etc. Moreover, data collectiontechniques described herein allow for data collection, furtherprocessing of the efficacy of the treatment protocol and furtheradjustments as necessary.

FIG. 2 shows a high level block diagram of a method of carrying outembodiments of the disclosed technology. In step 210, non-invasivemeasurements are made of electrical current in the brain of a testsubject. This is accomplished by way of electrodes placed on a testsubject, such as in a helmet shown in FIG. 1 . In this manner, EEG andERP signals may be recorded, measured, and analyzed. A single electrodemay be used to carry out the measuring in step 214, or a plurality ofelectrode pairs may be used in step 212. The position of the electrodesis known, and each electrode or a grouping thereof is placed over adefinable region of the brain, the region defined by a person carryingout embodiments of the disclosed technology. The region is defined as aspecific brain area of interest for the recording, as defined by aperson carrying out embodiments of the disclosed technology and may be aregion covered by a single electrode pair or as large as half ahemisphere of a brain. Electrodes may also be grouped into clusters,such as with a single anode surrounded by three or more cathodes, or asingle cathode surrounded by three or more anodes. Such clusters areelectrically connected, such that electric current flows non-invasivelythrough the proximal tissue from anode(s) to cathode(s), stimulating thebrain (stimulating, herein is defined as passage of electrical currentthrough the brain and includes increasing or decreasing neuron activityat a site).

While conducting step 210, typically, step 220 is also carried out whichcomprises providing sensory stimulus to a person. This may be done byway of, for example, the goggles shown in FIG. 1 for a visualstimulation 222, auditory stimulation 224, balance stimulation 226,biofeedback measurements 228, or other sensory stimulations known in theart. Definitions and examples of various types of such stimulations areprovided above, before the description of the figures.

Stress tests and peak performance tests may also be performed todetermine, for example, how many times a minute a person is able torespond to a stimulus, or how long a person can hold his/her breath orbalance on a force platform, etc.

Based on the electrical measurements, that is, EEG or ERP measurements,an abnormality in a region of the brain is determined in step 230. Anabnormality may be any of the following: electrical activity which istoo infrequent, too frequent, too low in amplitude, too large inamplitude, an improper pattern of electrical activity,inter-intra-hemispheric connectivity, electrical activity in the wrongportion of the brain for the stimulus given, or the like.

In step 240, based on the located functional abnormality, non-invasivebrain stimulation (such as tES) is administered at the region of theabnormality. In certain cases, the same electrode which was used tomeasure the electrical impulses within the brain is used to administertES or other electrical stimulation. This tES may be low intensityelectromagnetic stimulation, such as rTMS and low threshold TMStechnology or transcranial sound pulse technology. In this manner,accuracy of the stimulated region may be assured, as there is nodifference in the physical location on the head where the existingelectrical impulse was measured, versus where the new electricalstimulation is administered. The place of administering may be as littleas a single anode/cathode pair (or cluster), or may use multipleanode/cathode pairs (or clusters).

Whereby the device of FIG. 1 provides for collection of data, FIG. 3illustrates an embodiment of processing environment providing for theremote database and data analysis method and system operations. In thissystem, the local processing client 302 may be any suitable localprocessing device including but not limited to the collection ofmeasurement data, and/or one or more processing systems for executinginterface operations. For example, in one embodiment the localprocessing client may be a personal computer or a tablet computer havinga browser or application for executing the interface functionalitydescribed herein.

The network 304 may be any suitable network providing communicationthereacross. In one embodiment, the network 302 is an Internetconnection across a public access network, wherein it is recognized thatthe network may include a private and/or secure network, as well asnetwork exchanges via one or more service providers. The network 304operates to facilitate the communication of data between the localprocessing client 302 and the server-side network processing clients306.

The server-side network processing clients 306 may be any suitablenumber of network-processing devices. In one embodiment, the client 306may be a dedicated processing server, wherein in another embodiment, theclient 306 may be any suitable number of distributed computer resourcesfor processing operations as described herein.

Tests are used to measure psychological characteristics of a testsubject. The purpose of at least some of these tests is to assess theability of the test subject to automatically and fluently performrelatively easy or over-learned cognitive tasks relevant to the abilityto process information automatically or rapidly and measure executivefunction complex decision-making capacity. Test can be performed on oneor more suitable devices, such as a processing device providing adisplay. Such tests include, but are not limited to, trails making test,grooved pegboard, symbol-digit test, digit coding, symbol search, Strooptest, finger-tapping tests, categories test, Wonderlic tests andWechsler subtests, Wisconsin Card Sort Test, matrix reasoning, RavenProgressive Matrices tests, and/or components of the neuropsychologicalassessment batteries. Still another type of test is a test ofmalingering (e.g., TOMM) which can be part of a comprehensive assessmentof both mTBI (mild traumatic brain injury) and PTSD, as such tests aidin determining actual impairment resulting from neurophysiologicimpairment as opposed to subject feigning or exaggerating. Such testscan assist in minimizing false positive mTBI diagnoses. Psychologicalquestionnaires, for example a set of questions designed to diagnose aparticular psychological disorder, such as PTSD, can also be included incomputerized or hard copy form.

An additional component, a single pulse (0.9-1.5 tesla) fixed orvariable Hz setting transcranial magnetic stimulation (TMS) device maybe linked to a voltage isolator with linked amplifier for synchronizedEEG, ERP and/or electromyogram (EMG) recordings or low thresholdmagnetic stimulation (lt-TMS) that operates independent of a voltageisolator. The amplifier (such as amplifier 152 of FIG. 1 ) may be amultichannel amplifier for multiple modality physiological measurements(e.g., EMG, ERP, EEG, temperature, blood volume pulse, respiration, skinconductance, EKG, blood pressure, etc.). Sensors for each physiologicalmeasurement may also be connected to the amplifier, for example as ameans to collect measurements from a test subject. TMS and transcranialsoundwave pulse stimulation are non-invasive techniques utilizingmagnetic fields to create electric currents in discrete brain regions.Typically, during TMS, a time-pulsed magnetic field is focused oncortical tissue via a coil placed near the area to be affected (e.g., M1, Dorsolateral Prefrontal Cortex (DLPFC)). TMS can be utilized forvarious measurements of intracortical inhibition and facilitation, forexample short interval intracortical inhibition (SICI), long intervalintracortical inhibition (LICI) and contralateral cortical silent period(CSP). Such measurements can aid in differential diagnosis betweenindividuals with mTBI and mTBI with PTSD. Any commercially available TMSdevice known in the art may be utilized. For some embodiments, the TMSdevice utilized is portable.

Low intensity electromagnetic stimulation has been shown to have certainand safe clinical value, with rTMS technology being one example. Lowthreshold stimulation below that of rTMS available in this device iscalled low threshold transcranial magnetic stimulation (lt-TMS). Thiselectromagnetic stimulation is one of the methods utilized in thisdevice yet with the added benefit of being able to apply suchstimulation to one or more location points on the scalp within a givensession or predesigned treatment sequence to stimulate along a neuronalnetwork rather than one location along that network.

The lt-TMS is delivered using either a pulsed (or pulse-train) orsinusoidal electromagnetic stimulation waveform and is delivered basedon the manual operation of the device or programed delivery based ondeviation measures from a normative or comparison database of collectedEEG and ERPs produced by the same device. The device is designed torecord electrophysiology allowing for a pre and post measure of suchelectrophysiology when utilizing any of the stimulation types within thedevice (tDCS, tACS, tRNS, lt-TMS).

While rTMS has limitations of stimulation rate below 20-30 Hz due toheat, the selection of lt-TMS within the device allows for longersession durations and faster stimulation.

In one embodiment, lt-TMS delivery can be performed manually at eitherone or multiple locations. Delivery at multiple locations can be donesimultaneously or in timed preselected or preprogrammed sequence oflocations. Another benefit of lt-TMS is the real time measuring of theelectrophysiology before and after stimulation. The electrophysiologycan also be measured as part of a preprogrammed stimulation sequence.Furthermore, lt-TMS electromagnetic stimulation permits longerstimulation durations without heating the neuronal tissue and with alarger range of frequency selections 0.1-100,000 Hz.

FIG. 4 shows a perspective view of a helmet with electrodes used inembodiments of the disclosed technology. FIG. 5 shows a bottom view ofsuch a helmet. The helmet 400 comprises multiple electrodes, such aselectrodes 442, 444, and 446. As can be seen in the figure, a pluralityof electrodes are spaced apart around the interior of a helmet or otherpiece of headgear and are adapted for both reading electrical activityfrom the brain of the wearer and delivering new impulses. That is, byway of a single electrode, plurality thereof, cluster of electrodes, orplurality of clusters, a joint brain electro-analysis and transcranialelectrical stimulation system (tES) comprises a plurality ofspaced-apart removable and replaceable electrodes arranged in an item ofheadgear. An electroencephalography device (such as an EEG) is wired toeach of the electrodes, as is a transcranial electrical stimulationdevice (at the same time or alternating by way of a switch orplugging/unplugging a cable between the devices). In one embodiment,using lt-TMS, the stimulation sensors are smaller than conventionallylarger (25-35 cm2) in order to provide both a quality electrophysiologymeasure and to deliver a more focal stimulation.

Due to the know dielectric properties of skull and scalp tissue thedevice permits a dose-response adjustment such that the user can adjustfrequency and intensity according to measured electrophysiology changestaken at the stimulation location or all scalp locations (e.g., EEGamplitude, EEG coherence, ERP amplitude and latency) and accordinglyadjust stimulation thresholds to a level of desired neuronal tissuemodulation.

A cable 450, which will be discussed at greater length with reference toFIG. 6 , allows for electrical connectivity between the electrodes andeither or both of a tES and EEG device. Further, a visor 460 isintegrated with the helmet in embodiments of the disclosed technologyfor optical stimulation (e.g. a video monitor). The device cap/helmet isconstructed with an optional extension cable and materials such thatsimultaneous magnetic resonance imaging can occur while the device is inuse.

Upon measuring an electroencephalography anomaly in a brain region withthe electroencephalography device, transcranial electrical stimulationis engaged to at least one anode and at least one cathode electrode tothe brain region where said anomaly was measured. Additional devices, asdisclosed above, such as a force plate, visual stimuli utilizinginteractive games and tests, and the like, may also be utilized. Thetranscranial electrical stimulation device, in embodiments of thedisclosed technology, is engaged only when either a) data from theelectroencephalography device indicates that electrical impulses in thebrain are outside a predefined range/threshold of where they should beor where is desired by the administrator of the device; and/or b) whenthe additional physiological characteristic, as measured with anotherdevice disclosed in the specification herein (such as an EMG device,balance plate, pathological test, etc.) is out of range of a predefinedallowable threshold. Thus, the ability to administer tDCS may be limitedby the above factors and, as a safety measure, may be further limitedautomatically by way of pre-programmed instructions in a computer device(see FIG. 8 ) or manually by way of a physician or other clinicalpractitioner relying on such data.

The device delivers subthreshold polarization that potentially causespolarization at the soma and thereby obtains deeper brain sourcepenetration and delivers a greater effect with a least intrusive and ina safer manner than high intensity stimulation. The device permits aprogramming of stimulation type (tDCS, tACS, tRNS, lt-TMS) deliveryinterrupted by electrophysiology measures (e.g., EEG, ERPs) in order toascertain the relative difference each stimulation type has on neuronalactivation or suppression such that the most efficient and effectiveintervention can be then applied for the individual.

Referring further to a force plate (which includes a “balance plate” inembodiments of the disclosed technology), the device is used as follows.The force plate collects (and may record) balance and/or postural data,such as center of pressure, sway movement, and movement velocity toanalyze vestibular and balance function under different test conditions(e.g., unstable foam pad and eyes closed). For some embodiments, thebalance plate may be moved without the need for recalibration, forexample use in outdoor settings (e.g., sports, military arena).Collected data may be synchronized with visual input stimuli, EEG, ERPand/or other parameters for time locked variance measures associate withbrain dysfunction. In some instances, visual stimuli are provided to asubject while the subject utilizes the force plate. The visual stimuliproduced may be an “immersive environment”, for example a virtualreality 2- or 3-dimension moving “room” displayed through a virtualreality headset. The data collected from the force plate is used, inembodiments of the disclosed technology, for neurophysiological traumaassessment and/or rehabilitation training.

As further seen in FIG. 4 , the anodes and cathodes may be in a cluster420 and 440. The clusters shown are by way of example. That is, oneanode (e.g., 444) may be surrounded by three or more cathodes (e.g.,442, 446, and others), or one cathode may be surrounded by three or moreanodes. Anodes and cathodes have opposite polarity, and where neuralactivity is too high in a region, a cathode may be used to suppressactivity. Where neural activity is too low in a region, an anode may beused to increase activity. This may be done between two electrodes, acluster, or a plurality of clusters. In two different regions, it may bedesired, in embodiments of the disclosed technology, to stimulate (orde-stimulate) simultaneously. In this context, “simultaneously” may bedefined as being at the same time or alternating. Different rates ofstimulation at each region may also be used, as necessary. That is, tworegions that should not be linked, in fact are. By firing at differenttimes or rates, in different regions (at the second region, firing from0 to 180 degrees off, in a phase between two firings of the firstelectrode), two synced regions may be brought out of phase. This maynormalize brain activity as regions of the brain require specific phasesimilarities and differences depending upon their relative function.Similarly, by firing at the same time, two out of sync regions may bebrought in phase. Now, the two regions are said to have coherence.Biofeedback (a user viewing his/her own EKG, EEG, ERP, or otherindicators of physiology function) may be utilized in conjunction withthe tDCS, so as to give the user the ability to consciously control hisor her brain or other physiological activity to help the healing processwhen attempting to normalize brain or physiological function (e.g.,heart rate variability) activity.

The electrodes may be separable, so as to be individually placed, or maybe within a sized EEG cap or helmet (such as helmet 100 of FIG. 1 ). Theelectrodes, which can also be used as anodes and cathodes for purposesof tDCS, may be directly connected to one or more stimulation devices(e.g., tDCS or CES stimulation) and/or measuring devices (e.g., EEGrecording device) simultaneously, or via a switch or removable plug toswitch between such devices. When measuring EEG/ERP readings (electricalimpulses from the brain of a user), various activities (stimuli orphysiological measurements) may take place simultaneously. A fingerdepression device may be used, and others such as a force platform,heart rate monitor, EMG (muscle electric potential), interactivebiofeedback devices allowing the user to monitor internal activity(directly or by way of a game used to control by way of biofeedback),and the like. These measurements may then be compared against a databaseof known human population normative values as indications to determine adeviation from normal function, check the deviation against what isbeing monitored by way of EEG measures and abnormalities of electricimpulses in the brain, and in some embodiments, a correlation may bemade to determine brain abnormalities associated with differentdysfunctions. In other embodiments, the brain abnormalities will serveto verify a particular dysfunction. In still further embodiments, basedon prior determined data of brain electrical abnormalities for aspecific pathology, tDCS or other electrical stimuli (e.g., CES) is theninduced at a region where the brain abnormality is measured.

For example, a database may contain reference EEG components for normaland known pathological results (e.g., IED blast brain trauma, motorvehicle accident brain trauma, Attention Deficit Disorder, Alzheimer'sdisease). In some instances a database may comprise subcategorization ofdata from collected EEG and ERP data. Comparison of subject EEG and ERPresults to such databases can allow for EEG and ERP analysis as part ofthe diagnostic process. Source localization methods (to determinespecific regions of interest and dysfunction) may be accessed forselected EEG and ERP components.

When transcranial electrical stimulation (tES) is used as a result ofthe above measures, the current may be via the EEG electrodes or can bedelivered by other anode and cathode electrodes (i.e., anode sensors orcathode sensors placed from a different system) designated for tEStreatment. For example, sponges may be attached to graphite compositesensor pads sized for anode and/or cathode to ensure proper contact withthe subject. The tES device, in embodiments of the disclosed technology,directs anodal or cathodal non-invasive brain stimulation to one or moreof the connected site locations on the subject. Stimulation can bedelivered as transcranial current, or other effective current type, inamounts between about 0.25 mA and 6.0 mA.

Upon measuring an electroencephalography anomaly in a brain region withthe electroencephalography device, transcranial direct electricalstimulation is engaged to at least one anode and at least one cathodeelectrode to the brain region where said anomaly was measured.Additional devices such as a force plate, visual stimuli utilizinginteractive games and tests, and the like, may also be utilized.

As used herein, the tES may include, but is not expressly limited to,transcranial direct current stimulation (tDCS) or transcranialalternating current stimulation (tACS). The data collection techniquesand operations, as described in U.S. Pat. Nos. 8,239,030; 8,380,316; and8,838,247 are herein incorporated by reference.

The data is collected and thus provided to one or more remote dataprocessing systems. These remote data processing systems may beconnected via a networked connection, including in one embodiment anInternet-based connection. In additional embodiments, the networking maybe via a private or secure network. Wherein, it is noted thatInternet-based connections include the processing of security featureswith the data, to insure the privacy of the data during transmission.

For example, one embodiment may include a data collection computingdevice, such as a personal computer or other type of processing device,operative to receive the electrophysiology data. The processing devicetherein provides for the encryption or inclusion of security features onthe data and the transmission to one or more designated locations. Forexample, one embodiment may include the compression of the data into a“.zip” file.

The server further provides for the storage of the data and retention ofdata information. In this embodiment, the server creates a postscriptformatted file, such as a PDF file and the database is then updated toinclude storage of this information. In one embodiment the databasefurther includes enhancements to maximize storage, including determiningif the data to be stored is duplicative. If the data is duplicative, asingle data link can be provided, but if the data is not duplicative,then separate access to the data is provided.

The data acquired from the device may be processed locally or acrossnetwork. In a typical embodiment, the user or client is a doctor orother medical specialist having the ability to review, understand andadvise a patient based on the data generated in the reports. As notedabove, the data generated in the reports relate to the electrophysiologydata acquired from patients.

The complete system consists of a wireless amplifier equipped to recordartifact free electrical signals from the brain and heart and alsoposition in space using a nine or greater accelerometer. This samedevice is configured to deliver electric current back to the sensorsthat are in contact with the scalp in order to facilitated non-invasivebrain stimulation. Sensors make contact with this skin using either drysensors or electro dermal gel or saline impregnated sensor forconsistent sensor to skin connectivity measured by impedance.

The software provides for automated data collection using scriptsoftware and self-guided instructions. The software sends the resultingdata for algorithm processing either on the CPU or on a dedicated secureserver through an internet connection. This data is processed on the CPUand processed either on the installed database and processing softwareor transmitted to the cloud-based server where processing takes place.

The data analysis is returned in a report format showing physiologygraphics and interpretive results from which the user can makeintervention or diagnostic decisions. Several comparison databases canbe selected from within the software to provide a comparison measure forthe data analysis. Pre-set EEG training protocols (e.g., theta:betaratio training for attention; alpha:theta ratio training for relaxation)are configured for automated home or clinic based training.

Individual baseline data can also be utilized so that the individual'sdata can be compared to an earlier data sample. An example of this is aprofessional athlete having his or her pre-season baseline that is usedfor comparison following a concussion. This is particularly useful forsingle-subject design research of change over time and interventionresults. Group databases such as peak performance or pathologycomparison databases (i.e., Alzheimer's disease sample database) arealso available for selection and data comparison. Intervention optionsinclude real-time noise and artifact removal algorithms that permit EEGand ECG training devoid of movement and other disruptive artifact orsignal noise. Individual differences from the selected comparisondatabase permits specific or individually derived interventions asnon-invasive brain stimulation (e.g., tDCS/tACS) and brain computerinterface (sLORETA/eLORETA brain computer interface, wavelettime-frequency neurofeedback, event-related potential neurofeedback;Brodmann Area selection, neurofeedback, neuro-network brain computerinterface) and peripheral biofeedback such as heart rate variabilitybiofeedback).

The brain computer interface or neurofeedback can include any number ofoperations or techniques, including for example low resolution brainelectromagnetic topography source localization feedback and surfaceelectroencephalography amplitude or phase or coherence feedback.

The user receives report and intervention information from cloud-basedserver interface or from optional embedded software on the CPU for usagewhere internet connectivity is not possible.

The results of the data analysis include a protocol that directs thenon-invasive brain stimulation sensor placements and current parameters.These stimulation protocols can be manually or automatically selected toprovide the user with both brain compute interface training and brainstimulation or brain modulation interventions.

The rapid assessment and re-assessment of the brain and other measuresincluded in the physiology measurement battery allows for rapiddetermination of brain computer interface training location andfrequency protocols and also brain stimulation or modulation usingelectric current. The re-assessment quantifies the difference from thebaseline measure in order to generate a report showing the change madeby either or both brain computer interface and electric current brainmodulation.

The re-assessment then provides an updated intervention protocol.Protocols will vary based on the assessment results such that thedifferent locations on the scalp may be stimulated with differentpolarity at the sensor and with more or less milliamps than one another.Users can manually define scalp location, polarity at the sensor, andmilliamp levels and duration at each location. Users can also selectfrom pre-defined protocols to increase or decrease regional neuronalactivity.

The same data analysis report provides illustration and instruction onthe current flow through the brain tissue in order to further quantifythe cortical excitability relevant to the users clinical or performanceintent. Current flow reporting aid the user with further and morespecific brain modulation targeting protocols using Talairach or otheravailable coordinate source location libraries and Brodmann Areas. Theavailability of the data analysis and reports on the web portal allowsfor telemedicine access and review.

The sensors permit real time stimulation with electrical current andsimultaneous recording of EEG using signal filters that remove theelectrical stimulation and permit only the EEG and event relatedpotentials to be recorded and processed. This feature permits the userto combine targeted brain stimulation with brain computer interfacetraining using real time artifact correction. Simultaneous neurofeedbackwith stimulation allows for data analysis showing the focal changes ormodulation in the brain from the individual or combined interventionmodalities.

FIG. 5 illustrates a circular data flow diagram representing thecircular operations described herein. Step 500 includes the assessmentand re-assessment protocols, such as EEG, ECG, Balance, ERP, etc. Step502 is the automated data analysis on a CPU or networked server. Step504 is the report output, which may include output in graphical formatwith interpretation data. The report 504 may further include targetedbrain stimulation protocol, functional training protocol with braincomputer interface.

Continuing in the cycle of FIG. 5 , step 506 is the automated or manualselection of brain stimulation protocol and/or brain computer interfacetraining protocol. Step 508 is an optional real-time assessment duringbrain stimulation or brain computer interface training. Step 510provides automated reporting that reflects changes following brainintervention(s) with report output, which can be available to a userincluding HIPAA-compliant web or network portals.

FIG. 6 shows electrical pathways to electrodes within a helmet of anembodiment of the disclosed technology. Electrical connections (such asconnection 470) provide an electrical pathway to and from each electrodeand join at a cable 472 housing all electrical connectors between eachelectrode and an amplifier or other equipment for sending and/orreceiving electrical impulses. Each electrode, such as electrode 446comprises the electrode itself (typically, a metal or other knownconductor, the conductor being removable from an electrode housing 448with disposable electrode boot 449 in embodiments of the disclosedtechnology) with a hole 447 for inserting conductive gel.

FIG. 7 is a side view of an electrode with disposable electrode bootused in an embodiment of the disclosed technology. An encasement 448,such as one made of hard plastic covers the electrode. The electrode 449is attached within the helmet 400. A disposable foam conductive patch isinserted, in embodiments of the disclosed technology, into an electrodesensor. Conductive gel permits a conductive connection from theelectrode, and by extension the foam patch insert, to the skin. Thisconnection permits both the recording of cortical electrical activityand the delivery of anodal or cathodal direct current. Two version ofthis electrode are available: (1) the first version is a soft rubberboot that can be wrapped around a hard plastic electrocap device. Thissoft boot slips onto any of the electrocap sensors and has within it aporous foam or sponge pad. The connective gel that is inserted into theelectrocap hole also flows into this boot as shown in the art. (2) Asecond version is a harder plastic, carbon, or graphite materialreplacement sensor that connects to any wire harness for EEG/ERP and maybe built into a helmet or softer cap.

In an embodiment of the disclosed technology, a single interface is usedto control EEG, ERP, and tES and is electrically or wirelesslyconnected/engaged with any one of or a plurality of inputs including ECGsensors, a balance plate, a headset, a tES cap, or the like. Between theinput devices and the interface may be a voltage isolator and/oramplifier. The interface, or a separate computational device may be usedfor data collection and analysis from the EEG/ERP cap and other inputs.Visual images may be displayed on a headset and visual and auditorystimuli may be provided by way of a monitor and speakers, respectively.

FIG. 8 illustrates another embodiment of a device for collecting dataand providing user feedback. This device 600 includes earpieces 602 withspeakers 604. The device 600 further includes a top cross-bar 606 andside-bars 608, the bars, 606 and 608, having a track 610 thereacrosswith sensors 612 disposed therein. The device 600 additionally includesa hinge 614 for the side-bars 608. Further embodiments include anarticulating arm 618 having a lens 620 thereon.

The headgear 600 may be composed of one or more suitable materials,including plastic, metal or carbon fiber by way of example. Theearpieces 602 are representative embodiments of engagement portionsproviding for engaging the user's head and securing placement of thesensors 612. In the illustrated embodiment of FIG. 6 , the speakers 604are disposed within the engagement portions of the earpieces 602,providing for the audio output of sound consistent with known speakertechnology. In this embodiment, the earpiece 602 and speaker 604 includecushioning 616 that not only improves user comfort in wearing thedevice, but also improves sound isolation of the speaker to minimize orreduce any ambient noise.

The cross bar 606 and side bars 608 include the track 610 that allowsfor the insertion of the sensors 612. The sensors 612 may be anysuitable sensors that connect into the track for electrical connectionwith the device 600. In one embodiment, the sensors 612 are dry sensors,where the dry sensors are attached using magnets for easy removal andreplacement in-between users and for alternate sensor or electrode typeattachments. The same system both provides EEG/ERP measures but alsodelivers brain stimulation using direct current and/or alternatingcurrent, as described above.

When worn by a user, the sensors 612 are in contact with the user'scranium, wherein the location of the sensors 612 can be adjusted bymovement of the sensor 612 along the track 610 within the cross-bars 606and 608.

The hinge 612, disposed on both sides of the cross-bar 606, allows forthe articulation of the of the side bars 608 away from or towards thecross-bar 608. Therefore, when worn by the user, the sensor 612 locationof the user's cranium can also be adjusted by the inward or outwardarticulation of the side bars 608.

In embodiments including the arm 618 and the lens 620, the headgear 600allows for the visual display of content on a lens, not expressly shown.The positions or location of the lens relative the user can be adjustedby the adjustment of the arm 618. The arm 618 includes wiring (notreadily visible) for providing an output signal to the lens. In oneembodiment, the lens may be a high-definition lens operative to providea visual output viewable by the user, where as described herein, theuser can be subjected to visual stimuli for feedback generation via theheadgear. In this embodiment, the lens operates similar to the visualdisplay goggles 104 of FIG. 1 or the visor 460 of FIG. 4 .

The above data collection and stimulation operations provide forclinical operations for improving and optimizing neurostimulationfunctionality. As used herein, a clinical operation is not expresslylimited to a clinic, such a medical or rehabilitation clinic, but canadditionally include an location wherein the use of the operationsdescribed herein are performed.

Moreover, the described methodology is functionally operational based onone or more processing devices operating in conjunction with one or moredatabases as well as a neurostimulation device as described herein.

FIG. 9 illustrates a generalized flowchart of one embodiment of theclinical application for neurostimulation. The methodology is quantifiedinto five categories, a data collection phase, a data analysis phase, anintervention selection phase, an invention delivery phase, and a changeanalysis. The phases noted in FIG. 9 are described in greater detail inFIGS. 11-15 below.

The methodology of the phases are performed using processing devices forcomputations, accessing databases for the acquisition of various datasets, measuring and testing devices for the acquisition of patientinformation, as well as computing interface(s) for data collection, e.g.patient questionnaire, as well as the utilization of the hereindescribed neuromodulation device for application of stimulation to thepatient.

Phase 902 is the receipt of input of the measured physiology data. Phase904 is the analysis of combined physiology and input data againstreference database and correlated neuronetworks. Phase 906 is thecalculation of individualized intervention protocols. Such protocols mayinclude, but are not expressly limited to, medications, supplements,neuromodulation modalities and placement, brain computer interfacesurface network or network feedback. Phase 908 is the display ofphysiology change, calculated either locally or remotely. Phase 910 isthe user selection or automated network-based or locally processedlaunch protocols. In one embodiment, the methodology is iterative,wherein step 902 is therein repeated.

FIG. 10 illustrates another flow diagram of multiple embodiments of theclinical use application described herein. While iterative in nature,the first illustrated step, step 1002 is the receipt of clinicalsymptoms based on checklists across physiology domains. Step 1004 isreceipt of diagnosis code by ICD or DSM. Step 1006 is receipt of currentmedications and any supplements taken by a patient. Step 1008 includereceipt of measurement data, such as but not limited to EEG, ERP,ECG/BVP Electrophysiology, and Vestibular Balance measures of rawphysiology and quantified data against normal database and clinicalpopulation database parameters.

In step 1010, the methodology provides for cross correlation of data todetermine good of fit for: brain neuronetworks; and central andautonomic nervous system disorders; and biomarker targets. This step maybe performed based on one or more processing devices accessing one ormore databases having data sets therein.

Step 1012 provides that good-fit determinations trigger specifictreatment protocols, such as the exemplary list of protocols forneuromodulation and biofeedback in FIG. 10 .

Step 1014 includes one or more processing devices providing hardware andsoftware required to deliver the neuromodulation and biofeedback fromthe various protocols. In another embodiment, or in addition to step1014, step 1016 provides for cloud-based or network-based processing ofinterventions and complete physiology analysis report.

Step 1016, provides for a change report. This report indicatesadjustments to the neuromodulation and biofeedback. Thereupon, theclinical operations revert back to a data collection phase of collectiondata about the patient to further iterate the treatments usingneuromodulation and biofeedback.

FIG. 11 illustrates one embodiment of the acquisition of data relatingto the patient, including measured physiology and other input data. Step1102 is the receipt of individual self-reporting or clinician-input ofknown or suspected information. One embodiment may include the inclusionof ICD or DSM diagnostic codes. This data may be received via anysuitable data processing interface, including via data entry a userinterface, accessing a patient database, or any other suitable means.

Step 1104 is the receipt of the input of symptoms. One embodiment mayinclude selection or recognition on a digital checklist. Symptoms, byway of example, may include lack of attention to detail, depressed mood,aphasia, memory loss, among others. The input of step 1104 may includeinput to any or all physiology systems of the patient.

Step 1106 is the receipt of electrophysiology measures. Exemplarymeasures include balance, EEG, ERP, ECG/BVP. Wherein receipt of thesemeasures may be acquired, in one embodiment, using the techniquesdescribed above, including techniques relating to FIGS. 1-7 .

Step 1108 is the receipt of individual or self-reporting or clinicalinput of supplements or medications taken at the time of assessment.Similar to step 1104, this input may be via any suitable interfaceincluding data entry or in another embodiment accessing a relateddatabase having the data therein.

The input of data may include additional data, wherein the steps1102-1108 are exemplary and not limiting in nature. Thereupon, in oneembodiment, the first phase (phase 902 of FIG. 9 ) is completed based onthe collection of data described herein.

FIG. 12 illustrates steps relating the second phase, phase 904 of FIG. 9. This phase relates to the analysis of data based on reference data.Not expressly illustrated herein, the methodology includes access to oneor more databases having the referenced data therein. The referencedatabase(s) may be locally or remotely stored, wherein access may beusing recognized database accessing protocol(s).

Step 1202 is accessing the user selected reference database. Thisdatabase may be selected in reference to an underlying or anticipatedcondition of the patient. The database may be selected based onrequested reference data for further processing of the input data. Forexample, the reference database may include peak performance data,Alzheimer's data, normal population data, specialized population data,etc.

Step 1204 provides for comparing physiology measure to correspondingreferenced sample data as may be acquired in step 1202. The comparing instep 1204 provides for calculating and displaying differences in thedata sets, in one embodiment.

Whereupon, in step 1206, input and physiology data are cross correlatedto determine central and nervous system locations (i.e. x, y, and zcoordinates within the brain with or without individual MRI imaging)that match or correlate with neuronetworks, diagnosis and functionaldeficits or strengths. Thereupon, in one embodiment, step 1206 generatesgeneralized location information for the application of neurostimulationbased on the data calculations described herein.

Scalp electrical potentials are recorded from the surface placedelectrode sensors and solutions (eLORETA, sLORETA) to the inverseproblem are used to source intracranial signals for using more than onemethod of localization. One example of this method to compute corticalcurrent density with optimized localization capacity and dynamicfunctional connectivity in the brain is by the use of eLORETA publishedby Roberto D. Pascual-Marqui. Reference: Pascual-Marqui. RD, Lehmann, D,Koukkou, K, et al. (2011). Assessing interactions in the brain withexact low-resolution electromagnetic tomography. PhilosophicalTransactions of the Royal Society A, 369, 3768-3784.

FIG. 13 illustrates steps relating to the calculation of individualizedintervention protocols, Phase 906 of FIG. 9 . Step 1302 is the EEGbiofeedback, such as a brain computer interface using EEG Phase, PhaseReset, Asymmetry, Coherence and Amplitude (uV) increase or decreasetraining based on surface readings compared to reference normal or otherreference database data.

Step 1304 is the EEG calculation source localization, e.g. sLORETA,biofeedback that identifies the individual voxels within the brain inorder to source the specific structures, Brodmann areas, nodes, and/orneuro-networks.

Step 1306 is the determination of heart variability biofeedback, i.e.maximum variation of heart rate, SDNN, RMSSD, frequency ratios,percentage cardiac coherence. This step may be performed by readingheart rate variability biofeedback information from the data acquisitiondescribed above.

Step 1308 is the determination of vestibular balance biofeedback, suchas by way of example velocity of sway and degree of sway. Similar tostep 1306, this data can be acquired based on the data acquisitiontechniques described above.

Step 1310 is the selection of neuromodulation protocols based on the x,y and z coordinate voxels and the identified neuronetwork tracks.

Upon selection, the protocol determines which surface location with beanode sensors(s) and which cathode sensor(s) and at what MV and timeduration (e.g. dosage). In one embodiment, the collections or groupingsof voxels can be selected by x, y, z coordinates or by drop down menuusing an anatomical library (e.g. structure name, network name, BrodmannArea).

Measurements and quantification of the physiology (e.g., EEG, ECG, ERPs,balance) are obtained using sensor array and other electronicmeasurement components (i.e., gyroscope, accelerometer) and processedwith algorithms for signal cleaning, artifact removal, and interpretiveanalysis against norm groups. Additional subjective data collected iscomputed for goodness-of-fit, such as noted below, to ensure ampleoverlap of objective and subjective data so that physiology findings isconsistent with symptom complaints or peak performance objectives.Voxels, or clusters or neurons, are targeted using source localizationx, y, z mathematics (i.e., sLORETA) and each voxel or mega-voxel (largercluster of voxels) are calculated for normalcy against normal or specialpopulation databases for several functional measures to include 1 Hzbins frequency amplitude, coherence, phase, phase reset. Those voxelsthat fall outside of set normal limits or targeted peak performancelimits (e.g., z-score+/−1.0) are listed for intervention targeting withcombined or in isolation neuromodulation techniques to include pulsed ornon-pulsed ultrasound neuromodulation, tDCS, tACS, magnetic field andbrain computer interface, balance training, heart rate variabilitybiofeedback, or other user defined and delivered interventions. Followthe intervention process a repeat assessment is calculated based on thedifferences across all physiology measures and differences acrosssubject/patient self-report data submitted. The cycle can repeat untilpre-selected normal or peak performance limits are achieved or the userchoses to disconnect the cycle of measurement, modulate, train,re-measure.

FIG. 14 illustrates steps of one embodiment of the user selected orautomated launch protocols, phase 908 of FIG. 9 . Step 1402 providesthat goodness-of-fit intervention protocols are listed for end userselection.

Goodness-of-fit refers to overlapping physiology biomarkers (e.g., low8-12 Hz EEB power in locations F3 relative to location F4; Brodmann Area25 z-score greater than −1.5) known to correlated with subjectiveselection of symptoms or signs (e.g., lethargy, flat affect, insomnia)and additionally current diagnostic labels (e.g., Major Depression, MildCognitive Impairment). A correlational analysis of the available databoth confirms the relationship between symptoms and biomarkers but alsocalculates a quantified degree of severity from a normal health samplegroup.

Step 1404 provides that interventions can be combined or used inisolation or in a preselected sequence. For example, a heart ratevariability biofeedback, tDCS neuromodulation, EEG brain computerinterface, balance biofeedback.

Step 1406 provides for any customized user defined intervention options.Step 1408 is the selection of a music library using neuroacousticentrainment technology or based on individual EEG patterns relative tonormal or special population database data. In one embodiment, audioentertainment may be specifically delivered via frequency tones orovertly to existing music or over selection of music from a librarybuilt into a headset device, such as the device of FIG. 8 .

In one embodiment, the methodology of FIG. 14 therein reverts back tostep 1402 for further modulation, adjustment, refinement or processing,if necessary.

FIG. 15 illustrates steps of the phase for calculation and display ofphysiology change, phase 910 of FIG. 9 . Step 1502 is the printed reportof all physiology measures and calculated change from pre-selectedassessment data set or baseline data set. In one embodiment, the reportprovides the deltas or changes in values over any number of selectedintervals. For example, one embodiment can be changes over a fulltreatment period. In another example, the changes can be from a previousor prior treatment iteration. Moreover, the printed report may bemodified or adjusted to customize for specific features or changes, asrecognized by one skilled in the art.

Step 1504 is the generation of web interactive HTML or similarnetwork-based interactive display showing results and item descriptions.The interactive display may include active links to associated content,such as scientific research, video data, etc. The display mayadditionally include graphical information plotting measured changesacross time periods.

A further method for neuromodulation therapy includes the methods andsystems described above, further including processing operations fordetecting a brain malady of a patient. Existing medicinal solutionsalone are limited in offering long-term symptom relief of brainmaladies, for example neurodegenerative dementia conditions. Whereasnon-invasive brain stimulation can offer relief to patients.

As used herein, a brain malady is any type of illness or other conditionassociated with the brain, including but not limited to dementia,depression, Alzheimer's, aphasia, mild brain injury, traumatic braininjury, etc. For example, front temporal dementia is a type of dementiawhere, when correctly diagnosed, responds well to non-invasive brainstimulation treatments. Effectiveness of these brain stimulationtreatments are predicated in large part on proper detection of clearregion(s) of interest within the brain and proper source localization ofstimulation therapy. Incorporating neurostimulation with additionalpatient testing and feedback systems, improves the effectiveness ofneurostimulation. Additional benefits can be found using imaging andother external factors, such as MRI readings and software analysis ofMRI data, by of example.

The method includes processing operations, building upon the datacollection noted above, for estimating a brain malady type and aseverity value for a patient. As described herein, the furtherneuromodulation therapy includes machine learning/deep learningoperations to generate reference data used to determine a patient'sbrain cognition status based on measurement data in relation to existingmeasurement data from varying sources.

The present method includes machine learning operations for generatingand using data sets for estimating the brain malady within a specificcategory and the severity value providing a range or estimate oflikeliness of said categorization.

Dementia includes loss of cognitive functions, a common example beingAlzheimer's and its degenerative state(s). Brain injuries can includemild traumatic brain injury (TBI) to severe brain injury, with stagestherebetween.

The present methodology builds upon existing testing and dataacquisition techniques to collect patient data, including the techniquesnoted above. Predictive accuracy with machine learning enhances thecollection of multiple non-invasive electrophysiological measures. Thesemeasures can include quantitative electroencephalography (qEEG) values,such as but not limited to absolute and relative amplitude, coherence,time frequency analysis, spectral analysis, and dominant peak EEGrhythm. Additional measures include event related potentials (ERP)recorded at each electrode location following time locked delivery ofdiffering visual and auditory stimulus types. These added ERP values canallow for including omission and commission error and response speed,which allows for adding performance values (e.g. body valuemeasurements) to the electrical measurements of the brain under restingand test conditions.

Additive computerized neuropsychological test values of effort onstandardized tests, for example MoCA, Symbol-Digit, Trails A and B, orblood and/or saliva values can also be body value measurements aiding inmachine learning calculations. Another body value measurement can be aforce plate measurement, e.g. block 226 of FIG. 2 , and balancecorrection speeds.

Based on the processing algorithms, the present method therein generatesa treatment protocol for treating or mitigating the brain malady. Thetreatment protocol includes transcranial stimulation, for example thestimulation devices noted in FIGS. 1 and 6 above.

FIG. 16 illustrates a flowchart of the steps of one embodiment of theneuromodulation therapy method. A first step, step 1602, is receivinginput data about a patient including brain value measurements and bodyvalue measurements. As used herein, brain value measurements relate tovalues acquired using helmet or other transcranial stimulation device,such as device 100 of FIG. 100 . The brain value measurements canadditionally include external measurements values, such as measurementsor data acquired from MRI or other scanning technology.

As used herein, body value measurements relate to any values associatedwith the patient not directly found within the brain value measurements.By way of example, body value measurements can include heart rate or EEGvalues acquired using electrodes or blood volume pulse sensors placed onthe patient's skin. Another example of body value measurements can bebalance plate measurements from a patient standing on a balance plate.Another example of body value measurements can be blood or tissuesamples from the patient. The above examples are exemplary in nature andnot an exclusive or exhaustive list of types of measurement values,whereby the body measurement values can include any additional values asrecognized by one skilled in the art.

Acquisition of the brain value measurements and the body valuemeasurements can be using techniques described above. For example, FIG.2 above notes of generating and receiving non-invasive measurements(brain value measurements) of electrical currents in the brain of apatient. FIG. 2 also illustrates inclusion of sensory stimuli assecondary (body value) measurements.

In a further embodiment, these values can be imported or acquired fromadditional or third-party sources, for example receiving blood samplereadings from a lab or downloading heart and cardiac rhythms from afitness tracker or third-party application.

The measurements acquired from the patient can include measurements fromone or more EEG electrodes on the patient's head and electrocardiographelectrodes on the chest and head and force plate movements under taskand balance conditions. Values from these measurements can include, butare not limited to, qEEG amplitude, sensory and cognitive event relatedpotentials, phase, network coherence, dominant peak EEG rhythm by brainregion, frequency band statistics (i.e. alpha1:alpha2 ratio), currentdipoles, source localization calculations (e.g. LORETA, LAURA),time-frequency analysis techniques (e.g. discrete and continuous wavelettransform, Fourier transform).

In the methodology of FIG. 16 , step 1604 is electronically analyzingthe input data relative to reference data, the reference data generatedusing machine learning and deep learning (ML/DL) operations. Thisreference data generation is described in a greater detail below. Theanalysis can include using decision tree nodes, the nodes generatedbased on the ML/DL operations.

Step 1606 is electronically determining a brain malady and a severityvalue for the patient. The brain malady, for example, can indicate thepatient is likely to have a mild TBI with a severity value estimatingthe likelihood within a specific percentage range. This determining stepis performed by at least one processing device using the patient dataand referencing the reference databases.

As described in further detail below, step 1606 can include generatingvalues predicting a cognitive category, also referred to as the brainmalady, with a diagnostic accuracy, also referred to as the severityvalue. In one embodiment, the cognitive category can include threecategories: dementia (AD), mild cognitive impairment (MCI), andsubjective cognitive impairment (SCI). It is recognized that furthercategories as recognized by one skilled in the art are within the scopeof the present disclosure.

In one embodiment, a computation value for the cognitive category withdiagnostic accuracy can be expressed as an area under a curve (AUC). Forexample, in one embodiment, the predictive cognitive category of personswith a diagnostic accuracy of 0.79 can be shown relative to:

AUC=0.96 (95% Cl), between AD and SCI.

AUC=0.89 (95% Cl), between AD and MCI.

AUC=0.92 (95% Cl), between MCI and SCI.

Based thereon, step 1608 is generating a treatment protocol for thepatient based at least on the brain value measurements, the brainmalady, and the severity value. The treatment protocol includesparameters for transcranial stimulation using an EEG, including forexample TMS or any other suitable type of stimulation. The treatmentprotocol further includes voltage strength and duration values, as wellas location instructions for where to apply stimulation to the head ofthe patient.

In one embodiment, the generation of the treatment protocol can includeusing a look-up table or other reference table having recommendedtreatment protocols relative to the brain malady and related data. Thetreatment protocol includes transcranial stimulation parameters usablefor step 1610 below. These parameters can include current levels andduration instructions for transcranial sensors, as well as sensorplacement instructions.

Therefore, step 1610 is applying the transcranial stimulation to thepatient consistent with the treatment protocol. Accordingly, the patientis the given a personalized treatment protocol in accordance with thepatient's brain malady, for example a specific type of depression istreated with a designated tES protocol.

The present method includes transcranial stimulation via at least oneanode and at least one cathode electrode to the brain of the patient.The method augments this non-invasive stimulation based on the brainmalady and severity value using, in one embodiment, a region of interesthierarchy. Herein, the regions of interest of the patient's brain arepresented in x-y-z coordinates of the brain based the patient's headmeasurements and in relation to best fit automated stereotaxiscoordinate atlas library or multiple libraries that include but are notlimited to Talairach, Tournouz, and Montreal Neurological Institute. Thepatient brain source imaging locations can also account for minimal normsolution and weight minimum norm solution (e.g. LORETA, LAURA). Herein,the operations may utilize similar techniques noted above in step 1206of FIG. 12 .

Application of the transcranial stimulation may be similar to step 240of FIG. 2 , herein using the treatment protocol accounting for thedementia type and severity value for the patient.

The present method can additionally iterate for on-going treatments.Another embodiment can include the method reverting to step 1602 foradditional data gathering after (or during) the treatment protocol. Asnoted by the dashed line, this iterative process can then proceed againto step 1604 to further analyze the input data. Herein, step 1612provides for updating the brain malady and severity value for thepatient relative to the initial treatment protocol. Based thereon, step1612 may include modifying the treatment protocol as needed andproceeding to step 1610 for further application of the neuromodulationstimulation.

In one example, a patient may receive a clinical diagnosis ofdepression. The patient dons the EEG headset, e.g. FIG. 1 , and theheadset collects electrophysiology raw data. This data may be in analogformat, subject to conversion to digital format. The patient is alsosubject to additional testing, such as an EKG, balance plate, or othertesting protocols as noted above. This also collects further data.

The processing of the headset data (brain value measurements) and bodyvalue measurements can produce a report for the patient's doctor. Thedata analysis includes the dementia type, which in this example canindicate a subtype depression with an underactive left front brainregion and the right front brain region being overactive. Based thereon,the electronically generated treatment protocol for the patient could,in this embodiment, direct neuromodulation to increase activity of theneurons on the frontal left and inhibit over activity of the front rightregion. Tracking the treatment protocol can further include specificityor sensitivity to relate different types of brain maladies, for examplespecific type of depression, e.g. atypical or mixed depression.

FIG. 17 illustrates one embodiment of the processing environment for theML/DL operations. The processing environment includes a local processingclient 1702, such as allowing for a clinician or other user to enterpatient data. This local client 1702 can also include automated datagathering operations, such as being connected to an EEG and other datacollection devices.

One embodiment includes a local executable collecting data, either viadata entry or via data capture, encapsulating the data into a usableformat, and transmitting the data to a server 1706 or other networkprocessing device via a network 1704. The server 1706 can be any numberof processing devices allowing for machine language and artificialintelligence processing operations, such as in a cloud computingenvironment. The server 1706 further includes access to any number ofreference databases usable to best-fit analysis or supplementing themachine learning operations.

The network 1704 can be any suitable network, such as a private orpublic network, for example the Internet. Transmission of data caninclude encryption and other techniques for masking or otherwisemaintain patient confidentiality.

The server 1706 performs processing operations as noted herein,including as described in greater detail in FIG. 18 . Therein, theprocessing operations generate the brain malady and severity value forthe patient, as well as the treatment protocol based thereon. Thesevalues and the treatment protocol can then be transmitted back to thelocal client 1702 via the network 1704.

At the local client 1704, a clinician or other user can then providetranscranial stimulation to the patient consistent with the treatmentprotocol. In one embodiment, the treatment protocol can be manually setby the clinician. In another embodiment, a further executable softwaremodule may then translate the treatment protocol into neuromodulationinstructions for the transcranial stimulation treatment given to thepatient.

In one example, the patient may have the transcranial stimulation deviceplaced on his or her head. The local client 1702 then runs the treatmentprotocol by applying transcranial stimulation at different nodes on thepatient.

The above described method and processing system uses ML/DL operationsfor improving patient diagnosis and efficacy of treatment with tailoredtreatment protocols. FIG. 18 illustrates the two-part process, with afirst part being the ML/DL training to generate the reference data andthe second part utilizing the decision tree generated by the first parttraining.

Step 1802 is collection of input patient data. As described in detailabove, this patient data includes target variables and predictorvariables. The target variables can be clinical data associated with thepatient, predictor variables can be measured variables predictive forthe patient, for example EEG derived quantities.

Step 1804, the ML/DL executable applications learn relationships betweentarget and predictor variables using regression task processes and/orclassification task processes.

The machine learning model consumes multiple electrophysiology andlaboratory values (for example peak amplitude of alpha at location fromPz, O1, Oz, O2, blood homocysteine, APOE4, hA1C) that predict thedementia subtype and severity of a patient. The degree of sensitivityand specificity varies depending on the number of input values. Themachine learning operations refines the learning models based oncontinuous quantification of values. For example, even patients thatfall within a healthy category may be ranked with risk factors (e.g.,two copies of APOE4, low parietal peak alpha, delayed P300b). Theseoperations may additionally be performed or expanded using deep learningprocessing techniques.

In one example of ML/DL processing, the present system can build uponexisting relationships between varying data sets associated withpreviously-diagnosed patients. For example, one data analysis andlearning technique can use time frequency analysis and strengths ofoscillations at different frequencies over time acquired using an EEGheadpiece. In one example, a healthy patient can exhibit a dominantalpha at 10 Hz, but this is not found with Alzheimer patients. One dataanalysis technique may include taking alpha wave frequency images,converting them to grayscale, entering them into a computer vision modelcalled a convolutional neural network, and let the neural net discoverthe patterns that differentiate healthy people from Alzheimer'spatients.

Another machine learning operation includes generating a confusionmatrix showing classifier performance. One technique includes aleave-one-out approach, training the model on all but one subject andthen predict the label for the one held out subject. This techniqueincludes repeating the procedure until each subject was the one heldout. Therein, the matrix is the tally of those predictions. Theseoperations may additionally be performed or expanded using deep learningprocessing techniques.

Another data collection and analysis technique can include statisticsthat include t-score values for the patient relative to known healthyvalues. This can include capturing EEG frequency band (i.e. alpha,theta, beta, gamma) power values for various regions of the patient'sbrain using specific sensors of the EEG headpiece. The difference invalues and mapping of the values to specific regions is usable forpredicting brain disease.

The ML/DL computations and learning are based on available data inputs,for example EEG data, ERPs, ECG, vestibular balance values, symptomvalues, behavioral performance values. The ML/DL computations processthese input values to detect not only goodness of fit to diagnosis butmore importantly the better goodness of fit for particular non-invasivetES operations and protocols. tES can include, but is not limited to,tDCS, tACS, TBS, rTMS, ultrasound pulse stimulation.

In one example a 1 Hz left frontal rTMS may be shown to be likelyeffective compared to 10 Hz rTMS or to TBS. The ML/DL computations guidegoodness of fit of the brain malady with a more likely transcranialstimulation intervention option.

In one embodiment, the ML/DL operations are performed using a neuralnetwork model. In one embodiment, a deep network can include 4 layerswith multiple nodes per layer. The specific data sets per node can beadjusted consistent with machine learning and deep learning operations.

In this exemplary embodiment using four layers, the first layer caninclude 15 nodes using tan h activation for data analysis. A secondlayer can include five nodes, also using tan h activation. A third layerincludes three nodes and a output layer includes three nodes. In thisexemplary deep network, the nodes facilitate fitting the input data to acorresponding output of a decision tree or other reference databaseusable for determining a brain malady and severity value.

The layers and nodes per layer seek to generate determined results basedon the data. For example, can the data differentiate if the patient ismale or female with an at least 98% accuracy? The node processing thedata meets defined standards of data analysis with a result accuracy orreliability. For example, a node can determine with a high accuracy thegender of the patient. A next level or node can determine within anaccuracy level if the patient is depressed or not-depressed based oninput data. A third level or node can be if the patient satisfiesconditions for an Alzheimer's diagnosis, with a secondary inquiry oflevel or type of diagnosis.

The machine learning operations of step 1804 can use any suitable datafor predictive learning benefits. Therefrom, based on machine learningoperations, step 1806 is generating a decision tree based thereon. Thedecision tree can be a node-based decision model or any other suitablereferencing module for predictive values.

In one example, the decision tree can include step-wise decisionoperations for analyzing the patient data. Steps 1802-1806 are forbuilding and re-iterating the decision tree. Steps 1802-1806 can beiterative, updating and refining the machine learning as additionalsource data (patient and/or reference data) becomes available.

The input patient data is additionally usable for predicting a dementiatype for the patient. In step 1808, the patient data is processed togenerate predictor data. As discussed above, there can a large amount ofpatient data available, whereby the using of this patient data can bedirected to specific values or a grouping of values, or even aniterative process. For example, brain value measurements can be a firstlevel analysis with body value measurements for refining the analysis.In another example, brain value measurements and selected body valuemeasurements are initially analyzed with additional measurements (otherbrain value and/or body value) used for additional refinement.

Step 1810 is processing the patient data using the decision tree togenerate the predictor data. Step 1808 and step 1810 can be an iterativeprocess or a single data call operations.

Step 1812 is generating the estimate target data for the patient and atreatment protocol. Using the embodiments above, this estimate targetdata can be a clinical diagnosis value relating to brain disease, suchas the being categorized as pathology types that include AD, MCI, SCI,front temporal dementia, DLB, and depression. This value is the brainmalady, e.g. a dementia or depression type, and includes a severityvalue indicating a likelihood or reliability value associated with thebrain malady assessment.

Therein, based on the dementia type, step 1812 includes generating atreatment protocol. One technique can be a look-up table or otherreference database using known treatment protocols for specific dementiatypes.

The treatment protocol can also be modified based on any other suitableor available data, including the brain value measurements and body valuemeasurements. For example, if a subtype of dementia is estimated basedon a underdeveloped brain region, the treatment protocol may be modifiedto account for this brain value measurement. The treatment protocol mayalso include adjustments, modifications, or supplementation by a medicalprofession.

In one exemplary embodiment, a patient is subjected to multipleelectroencephalogram sensor placement on the scalp and electrocardiogramsensors on the body for collective EEG recordings, event relatedpotential recordings (e.g., P300a, P300b, N100, P200). These are inaddition to vestibular balance testing, neuropsychological tests, bloodtests, genetic testing with saliva, presenting symptoms, and individualdemographics. These recordings are processed for artifact correction andstatistical values that include z-score, t-score, and measures ofdifference from database reference groups (e.g., Alzheimer's disease,Bipolar Depression, Aphasia, Mild Cognitive Impairment, Normal, andAthlete Peak Performance).

Machine learning and deep learning calculations are applied to all theinput values that systematically differentiate and generate brain maladytypology and severity predictions. These machine learning predictionsrender sensitivity and specificity values against the library ofcomparison reference groups and matching to brain malady goodness of fitto particular disease endophenotypes.

By machine learning isolation of these brain malady subtypes, predictivevalues determine improved treatment intervention protocols. For example,in the case of a left frontal brain region hypofunction in the alphafrequency band, the intervention selection would list threeneuromodulation clinical options, those being (a) rTMS 10 Hz at locationF3, (b) brain computer interface enhance 8-10 Hz power at location F7and F3 with suppression of slow content power 3-7 Hz, and (c) tDCS anodeat F7 and cathode at Fp2 for 15 minutes of 1.5 mA direct current.

Following the matched treatment intervention, the recording of somenumber of repeated values (EEG, ERPs and ECG) are post processed andsecondly input into the comparison statistical analysis and machinelearning methods to generate measured improvements of the pre-treatmentbrain malady condition. Secondarily, the predictive positive response toindividualized treatment type strengthens the brain malady subtypingprediction. The machine learning accounts for all input values anddetermines which minimal number of values (e.g., peak alpha, elevatedfrontal theta EEG frequency dominance, SDNN, reaction time) still offerhigh predictive value, or the minimal number of test values that stillhighly predicts the brain malady and associated matching effectivetreatment option.

While the disclosed technology has been taught with specific referenceto the above embodiments, a person having ordinary skill in the art willrecognize that changes can be made in form and detail without departingfrom the spirit and the scope of the disclosed technology. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. All changes that come within the meaning and rangeof equivalency of the claims are to be embraced within their scope.Combinations of any of the methods, systems, and devices describedhereinabove are also contemplated and within the scope of the disclosedtechnology.

What is claimed is:
 1. A neuromodulation therapy method comprising:receiving brain value measurements and body value measurements relatingto a patient; electronically analyzing the brain value measurements andbody value measurements relative to reference data generated based onmachine learning operations associated with existing patient data andreference database data; based thereon, electronically determining,using at least one processing device, a brain malady and a severityvalue, for the patient; and electronically generating a treatmentprotocol for the patient based at least on the brain malady and theseverity value, wherein the treatment protocol includes a voltagestrength level, a duration, and position factors for transcranialsensors providing neurostimulation to the patient.
 2. The method ofclaim 1 further comprising: applying a transcranial stimulation to thepatient based on the treatment protocol.
 3. The method of claim 2further comprising: after applying the transcranial stimulation to thepatient, acquiring secondary brain value measurements and secondary bodyvalue measurements; electronically analyzing the secondary brain valuemeasurements and secondary body value measurements in reference to thereference data; and electronically determining a change value for thepatient relative to the brain malady and severity value.
 4. The methodof claim 3 further comprising: generating an updated treatment protocolfor the patient based on the electronic analysis of the secondary brainvalue measurements and secondary body value measurements.
 5. The methodof claim 1 further comprising: the brain value measurements includefirst electrophysiological values obtained from a brain of the patientusing at least one EEG electrode; and the body value measurementsinclude second electrophysiological values obtained from the patientincluding at least one of: an electrocardiograph electrode connected tothe patient and force plate measurements measuring task and balanceconditions of the patient.
 6. The method of claim 1, wherein the brainmalady includes at least one of: Alzheimer's, dementia, depression, mildcognitive impairment, and subjective cognitive impairment.
 7. The methodof claim 6, wherein the severity value assesses an accuracy level of thebrain malady of the patient.
 8. The method of claim 1, wherein the bodyvalue measurements include at least one of: a blood content value and abrain processing speed value.
 9. The method of claim 1, wherein theelectronic analysis of the brain value measurements and the body valuemeasurements includes using source localization models to isolate voxelswithin a brain of the patient to detect at least one region of the brainof the patient affected by the brain malady.
 10. The method of claim 9,wherein the electronic analysis includes referencing at least one brainmapping library.
 11. The method of claim 1, wherein the reference datais generated based on machine learning processing operations and deeplearning processing operations executed within at least one neuralnetwork.
 12. A neuromodulation therapy method comprising: receivingbrain value measurements and body value measurements relating to apatient; electronically analyzing the brain value measurements and bodyvalue measurements relative to reference data generated based on machinelearning operations associated with existing patient data and referencedatabase data; based thereon, electronically determining, using at leastone processing device, a brain malady and a severity value, for thepatient; electronically generating a treatment protocol for the patientbased at least on the brain malady and the severity value, applying atranscranial stimulation to the patient based on the treatment protocol;after applying the transcranial stimulation to the patient, acquiringsecondary brain value measurements and secondary body valuemeasurements; electronically analyzing the secondary brain valuemeasurements and secondary body value measurements in reference to thereference data; and electronically determining a change value for thepatient relative to the brain malady and severity value.
 13. The methodof claim 12, wherein the treatment protocol includes a voltage strengthlevel, a duration, and position factors for transcranial sensorsproviding neurostimulation to the patient.
 14. The method of claim 12further comprising: generating an updated treatment protocol for thepatient based on the electronic analysis of the secondary brain valuemeasurements and secondary body value measurements.
 15. The method ofclaim 12 further comprising: the brain value measurements include firstelectrophysiological values obtained from a brain of the patient usingat least one EEG electrode; and the body value measurements includesecond electrophysiological values obtained from the patient includingat least one of: an electrocardiograph electrode connected to thepatient and force plate measurements measuring task and balanceconditions of the patient.
 16. The method of claim 12, wherein the brainmalady includes at least one of: Alzheimer's, dementia, depression, mildcognitive impairment, and subjective cognitive impairment.
 17. Themethod of claim 12, wherein the severity value assesses an accuracylevel of the brain malady of the patient.
 18. The method of claim 12,wherein the body value measurements include at least one of: a bloodcontent value and a brain processing speed value.
 19. The method ofclaim 12, wherein the electronic analysis of the brain valuemeasurements and the body value measurements includes using sourcelocalization models to isolate voxels within a brain of the patient todetect at least one region of the brain of the patient affected by thebrain malady.